NNS 11.2 Beta

Author: OVVO-Financial

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: NNS
 Type: Package
 Title: Nonlinear Nonparametric Statistics
-Version: 11.1
-Date: 2025-02-17
+Version: 11.2
+Date: 2025-03-11
 Authors@R: c(
   person("Fred", "Viole", role=c("aut","cre"), email="[email protected]"),
   person("Roberto", "Spadim", role=c("ctb"))

NNS_11.1.tar.gz

@@ -33,7 +33,7 @@
 #'}
 #' @note
 #' \itemize{
-#' \item{} Typically, \code{(training.set = 0.8 * length(variable)} is used for optimization.  Smaller samples could use \code{(training.set = 0.9 * length(variable))} (or larger) in order to preserve information.
+#' \item{} Typically, \code{(training.set = 0.8 * length(variable))} is used for optimization.  Smaller samples could use \code{(training.set = 0.9 * length(variable))} (or larger) in order to preserve information.
 #'
 #' \item{} The number of combinations will grow prohibitively large, they should be kept as small as possible.  \code{seasonal.factor} containing an element too large will result in an error.  Please reduce the maximum \code{seasonal.factor}.
 #'
@@ -473,6 +473,8 @@ NNS.ARMA.optim <- function(variable,
     model.results <- pmax(0, model.results)
     lower_PIs <- pmax(0, lower_PIs)
     upper_PIs <- pmax(0, upper_PIs)
+    lower_PIs_is <- pmax(0, lower_PIs_is)
+    upper_PIs_is <- pmax(0, upper_PIs_is)
   }
 
   if(plot){

NNS_11.1.zip

@@ -61,10 +61,10 @@ NNS.copula <- function (
 
 
   # Isolate the upper triangles from each of the partial moment matrices
-  continuous_Co_pm <- sum(continuous_pm_cov$cupm[upper.tri(continuous_pm_cov$cupm, diag = FALSE)]) + sum(continuous_pm_cov$clpm[upper.tri(continuous_pm_cov$clpm, diag = FALSE)])
-  continuous_D_pm <- sum(continuous_pm_cov$dupm[upper.tri(continuous_pm_cov$dupm, diag = FALSE)]) + sum(continuous_pm_cov$dlpm[upper.tri(continuous_pm_cov$dlpm, diag = FALSE)])
+  continuous_Co_pm <- sum(continuous_pm_cov$cupm[upper.tri(continuous_pm_cov$cupm, diag = FALSE)]) + sum(continuous_pm_cov$clpm[upper.tri(continuous_pm_cov$clpm, diag = FALSE)]) 
+  continuous_D_pm <- sum(continuous_pm_cov$dupm[upper.tri(continuous_pm_cov$dupm, diag = FALSE)]) + sum(continuous_pm_cov$dlpm[upper.tri(continuous_pm_cov$dlpm, diag = FALSE)]) 
 
-  discrete_Co_pm <- sum(discrete_pm_cov$cupm[upper.tri(discrete_pm_cov$cupm, diag = FALSE)]) + sum(discrete_pm_cov$clpm[upper.tri(discrete_pm_cov$clpm, diag = FALSE)])
+  discrete_Co_pm <- sum(discrete_pm_cov$cupm[upper.tri(discrete_pm_cov$cupm, diag = FALSE)]) + sum(discrete_pm_cov$clpm[upper.tri(discrete_pm_cov$clpm, diag = FALSE)]) 
   discrete_D_pm <- sum(discrete_pm_cov$dupm[upper.tri(discrete_pm_cov$dupm, diag = FALSE)]) + sum(discrete_pm_cov$dlpm[upper.tri(discrete_pm_cov$dlpm, diag = FALSE)])
 
 
@@ -108,8 +108,6 @@ NNS.copula <- function (
 
   if(continuous_Co_pm == continuous_D_pm) return(mean(c(0, discrete_dep)))
   if(continuous_Co_pm==0 || continuous_D_pm==0) return(mean(c(1, discrete_dep)))
-  
- 
 
   if(continuous_Co_pm < continuous_D_pm) return(mean(c((1 - (continuous_Co_pm/continuous_D_pm)), discrete_dep)))
   if(continuous_Co_pm > continuous_D_pm) return(mean(c((1 - (continuous_D_pm/continuous_Co_pm)), discrete_dep)))

NNS_11.2.tar.gz

@@ -42,7 +42,6 @@
 #'
 #' # Produce a dendrogram as well
 #' results_with_dendro <- NNS.SD.cluster(data = A, degree = 1, min_cluster = 2, dendrogram = TRUE)
-#' plot(results_with_dendro$Dendrogram)
 #' }
 #'
 #' @export

NNS_11.2.zip

@@ -3,7 +3,7 @@
 
 
 
-[![packageversion](https://img.shields.io/badge/NNS%20version-11.1-blue.svg?style=flat-square)](https://github.com/OVVO-Financial/NNS/commits/NNS-Beta-Version)   [![Licence](https://img.shields.io/badge/licence-GPL--3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0.en.html)
+[![packageversion](https://img.shields.io/badge/NNS%20version-11.2-blue.svg?style=flat-square)](https://github.com/OVVO-Financial/NNS/commits/NNS-Beta-Version)   [![Licence](https://img.shields.io/badge/licence-GPL--3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0.en.html)
 
 <h2 style="margin: 0; padding: 0; border: none; height: 40px;"></h2>
 
@@ -56,7 +56,7 @@ Please see https://github.com/OVVO-Financial/NNS/blob/NNS-Beta-Version/examples/
     title = {NNS: Nonlinear Nonparametric Statistics},
     author = {Fred Viole},
     year = {2016},
-    note = {R package version 11.1},
+    note = {R package version 11.2},
     url = {https://CRAN.R-project.org/package=NNS},
   }
 ```

R/ARMA_optim.R

@@ -624,7 +624,7 @@ <h2>Stochastic Dominant Clusters</h2>
 <p>Further, we can assign clusters to non dominated constituents and
 represent the clustering in a dendrogram.</p>
 <div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" tabindex="-1"></a><span class="fu">NNS.SD.cluster</span>(<span class="fu">cbind</span>(x1, x2, x3, x4, x5, x6, x7, x8), <span class="at">degree =</span> <span class="dv">1</span>, <span class="at">dendrogram =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
-<p><img 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" style="display: block; margin: auto;" /></p>
+<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAEgCAMAAABcujGyAAAAzFBMVEUAAAAAADoAAGYAOjoAOmYAOpAAZpAAZrY6AAA6ADo6AGY6OgA6Ojo6OmY6OpA6ZmY6ZpA6ZrY6kLY6kNtmAABmADpmOgBmOjpmOpBmkJBmkLZmkNtmtttmtv+QOgCQOjqQOmaQZgCQZjqQkGaQkLaQtpCQttuQtv+Q29uQ2/+2ZgC2Zjq2ZpC2kGa225C227a229u22/+2/7a2/9u2///bkDrbkGbbtmbbtpDb27bb29vb2//b/9vb////tmb/25D/27b//7b//9v///+pI1/SAAAACXBIWXMAAA7DAAAOwwHHb6hkAAARGElEQVR4nO2dDXvbthHHKceelGXtmklptzb2tnbNJmUvyWqmyVpLkfT9v9MIgAAOJEVRAmTcmf/f8zSVQfJwOP6FNwJUsQeAMUVuBwDoAwIFrIFAAWsgUMAaCBSwBgIFrIFAAWsgUMAaCBSwBgIFrIFAAWsgUMAaCBSwBgIFrIFAAWsgUMAaCBSwBgIFrIFAAWsgUMAaCBSw5vEEursrbh7M/6fujxMpi8myYa4nJ/PHzy+Kovjiff8l6ug/748aO87u7awoXtaf/1VlPfnqfZ95DSlWn3fdrpSFxubTZ+O8oDeM0EI1HLwET12g26/N/SvmR27Q56+vUgh0XedlrjTSue0xrzkuUH15r0DrfPpIIdCgUE0HL0EmgZ7JgKpmH9wIG8+K2/4btCqSRJh46JSjDPeaP16snstdNoNiE0tQqGEOxpG3Bt29rcL6UqVW9+jHr4ur9/uPqsr7YkmT9qqVfvbaJP2juuR66c25Y/5KIsQqntdvqu93pdObB5NuQrld6Hrug22vVnXQWx690xeRjI3X10t6Sz5+Uznx7UNtphbKqpi8NlnfOvP+zH13sUgxWt791CqyKaL241N12TTwpSri9OML5cSHGQnZgcyCZJKFD0irUOSgLd+H7vY/gqwCrUKouL6338ybh7WvDGySKb1uOO3317d3/pi/0gu0+mRUtPvzy1/2bYGWzlwd4bZHv9Z31ftlauXJCy/Q+qh11QlUn7CZVTnZG+jP3HcXa98KgPfup2aR68yNH5Xr6qjPoS5LUbwIQkaL0s7MOO+zIAFpFYocrMvnPU7GowrUMvVaeV+XtSrazfv9/6r03zyESSqKf3j4vKjvb5VUqtAZC/4YudILVFUigQehQNXhh/3P+k6sbEvc9sjc1es6Y5Xn9GG38vehOt+cMm828ZNv39V/GPP0zIPFssVoedcsss2m/qQ/kByUfObq+uI1DRkpSiPatIQ2CxKQzkLZg9pB6nEqcgq0bmbL+h7ZbvcnNU6c7l2SubflV+9skr6Oqk0f81d6gZLI7jsEWh2+DhTU4ZG7q7fa3HTvDViJ1LXKylxFh3G6fnnzsCf6d2d2F4sUo+VdV5EDgVZZkxxMlWr+JV9cWpRWtGkJVRY0IK1C0YOr+tvhPE5FToFW5XHNir2xu+/tKTaJDm1Mkq4XdTI95q8cLFDjkukS2gg3PXIdtyDjPRkW2ISSlkOjZ7hM+0gUZs48UKywGKF3rSLXFxOB0hyMPfNvINCuzLpKaAJIWv5GoehB62Dhu9hpyNkHXRe+gF4OxfXfPi1CgbpmukOgU2/eXjmkiTfS/WwmoSZ/ryXQ7VF4V61NJ1CbsFZ1UGNAvvv0w6xwlRI983CxXACa3jWLbC8mAqU5HBdoO9o22WZBA9IqFD1ookE8TkVOgVZRc7Np9Y11ne+Ta1ByZecgqRpatgVahfQHVSHY5A6Pzq5BXe3tG/QBNSgNQNO7/hpUl/W0GrQd7Y4aNJjwDApFD9poOI9TkVOgtH6rb+za9oTIeMOUff3Fm44o+mPkyv5pptKOX23jv/urU1CHR+12cWAftDph8l3lxseZ73G2+6DNYtEANL1rFtn6qW2qIk6DHI4LtB1tWkKVRdgENQpFD5JJN+NxKrJOM+lJtc8LHzYzCK1jbe91FcaXSlyuASUC9cfIlUSgpON7625QdT/UiXMzWlXtknOg7VFboCeM4l2fu05vjOI7ikWK0fKuWeRmNo0cBtWgjWi7EtosSEDahWpGi3qcCg7zoGYazvVBG4MkNyvX2Q65Y+RK2gq6R50vrQeua1+J5K2/s6pD5eZBQ4+aI4ueedCOUbyf3uyeB20VyyU3vfupUeQgc/v0MZgHHdAH7fCBZkEC0i4UOWjKRzxORd4nSZ+/r9SiHzzQUfz1mzJsLfVzje/2nR0ld8xfGXTT6sUi77wHm0qzX/3HTJGQlSRv9URgy6O2QPWpX75vPUlSTgSDJJO1GdTW5smZ3cWiAWh6965Z5L3J0YjCPr9xOQwcxQfRtq22z8IHpF0ocrAun/c4FVhudy6rlA0ZOAQEejIr3V9wM93gokCgJ2On/x5j8RCAQE/n8zez1Gt2wCEgUMAaCBSwBgIFrIFAAWsgUMAaCHQgdLttx07f4Zt/B4MnAQoIdBj2GbR+4N2x03f45t+hGb5NumpNLBDoMILtth07fVNv/tXbLSFQCHQowXbb5k7ffSuJbNYlm5TdRurGYfMxXMJcTGYQ6B4CHUqw3bax01cRJpHNunRxnt1ITQ6HZxKBurVLIwcCHUaw3Tbc6duRRPbj0uXNdiN1uF3XfqQC/fCyvbdjnECgw9jRPcThTt/6U7h1LdyPW28QqXcyk8Otbb1BjhAoBDocsof4mEDJftxgi53f12IPd2zrtUCgGgh0OG4P8TGBkv24wSZlvzPQHu7c1lvnBoEqINBBhHuIg52+5oQgiezHPVCD3nq7h+ZNIVANBDqIcA9xuNNXEyS19+PaPqgWKDnc3NZLgEA1EOgwGnuIj0zUk/244Sje7vR3h8nHhiIhUA0EOoxgu+3RR51kP244D2oESg6HZ0KgLSDQgdDttscXi5DNumSTshtTkcNk5y4E2gEE+jhgadKZQKAXBpuU44BALww2KccBgV4abFKOAgIFrIFAAWsgUMAaCBSwBgIFrIFAAWsgUMAaCBSwBgIFrIFAAWsgUMAaCBSwBgIFrIFAAWsgUMCaxAKF3kFaTlPUqiimepviobcNQKAgLScpqtQbufUvlM67z4BAQVpOUdTuTr1OUG+uKQ/sUYRAQVpOUdR2UbXsa/2Kl3X4ukD3Wo38Ai3AAHLfpeE8uRo0vwcCEBSk8/qgWqrR5i5Cfg8EIChIT24Un98DAQgK0pObB83vgQAEBQkCHSOCggSBjhFBQYJAx4igIBFX9W9QrQ4Mz083l4n8HghAUJC8q+vJslQ/ghal0Pwlz++BAAQFybmqpjbVPGfZ/kmpc8xlI78HAhAUJOeqeo6pBLqGQJ8+goLUqkFXUa+qzl/y/B4IQFCQmn3Q8uBDohPN5SK/BwIQFKRwFB/9pur8Jc/vgQAEBQnzoGNEUJAg0DEiKEjE1ZX+4T5M1I8AQUHyrprh+3Zxzu/5MFqqnd8DAQgKUjAPqsA86AgQFKRgHlSBJ0kjQFCQvKtmBnQzw7P4p4+gIBFXNzPMg44EQUHCNNMYERQkCHSMCAqSd1Xv1qzAIOnpIyhIZB40Spktc7nI74EABAWJzIPGPUNqmMtGfg8EIChIrYn6ROaykd8DAQgKEpmoP+cZ50Fz2cjvgQAEBYksWI5bqtw0l4v8HghAUJBIE19gFD8WBAXpHFc3s4N1bf6S5/dAAIKCdIqrrpI9XM/mL3l+DwQgKEjeVSu/nia+Xs6MGlQ6goIULFgup33i2ysRq1XN7XOwYFkWgoIUTNSv1ZtF+vfFryZL1KDiERSkYKJ+89t7/V8fZTGHQKUjKEjBivrtq+VRgVb15zMIVDiCgkRW1Fejo9X8WBO/18ueIFDZCAoS3XY8VSP5uDVN+Uue3wMBCAoSFiyPEUFB8oOkV2Y3ErYdjwBBQWoJFNuOR4CgINWurvxUO7YdP30EBalVgyYyl438HghAUJAwSBojgoJEF4vM92VRRL0BnEHJ83sgAEFBChaLbGZTNRuaxFwu8nsgAEFBCp7Fq10fmGYaAYKCFAhUbY3HNNMIEBQk0sRP1WrP815g22EuF/k9EICgIAUr6ifLMzcfY8GyLAQFCdNMY0RQkCDQMSIoSBDoGBEUJOPqdjHHixtGhKAgoQYdI4KCBIGOEUFBarybKe5JPIeS5/dAAIKCZF1dmY1wJX4KcQwIClLt6tr+/Ez/m0WGmstIfg8EIChIxlXyAAmrmUaAoCC5aSabgMUiI0BQkKxAXcOO5XYjQFCQINAxIihIEOgYERSk0wR69Nfo8pc8vwcCEBQkK9CjL/dWuFnS9aHp0vwlz++BAAQF6RRX1Rsaaw69Ay9/yfN7IABBQTrF1cMdgf4V9cUT5sywjypGUUFKXING5wFDXAwxcemki0v75tqDfdD4PGCIiSEmLp12sR1MnbboiUdJYSiXpUcUaOY8YOgRDTFxKfF60P48YEiQISYuJV4P2psHDEkyxMSlxOtB+/KAIVGGmLiUeD0oAGlJvB4UgLQkXs0EQFogUMAaCBSwBgIFrDlpPSgAj42glYFgjECggDVWoJvZXK2iKyYpfm8OgFTUAtVPONWv0FziUScAZ1MLVD/gVL9Cg0edgBXkUad5II9pJsAJMg9qHshfQKAJug1lUdTrASPdWxXFVO/uj3VJNTebWapOO6Pev/q1rFSWtKDipi6JQI2OTtoO10+62dWyunvmJ8YiBapKtyqmwfKYs9D6fL4MnnGcaako5psvH+INJWK7+P0szaJgHWOtp4iykT6oUsH+zF/y6qbeWxddg5rtpLu7qqxxAtWGzNrXuC+ijr3prkd+o1fqK5PEo0S1gfnV6xQqUJZqaZ5fNr9g2VTtq6QtvLEZLVD7BaxuZpxAtSHTiYnryihD9S7seEOmKo7tXO3u0tw61zBHa1RZqu/9+WWz86BrvR9pM0u9KUlVDolqUGVtyqQGNU1OghrUyGH3S7xHqRo/1/lZx3bMUtagl6OseljRgyQry6odS9QHJS+hOIvt4uqeyD3CI1ug2F6xmceOJ94Pb0n1N6ZxNi//qHMze5ZiFG9MRLdjyUbxenRaJGgIS6PwNOpKQDqB7utfKD7xPR8hj/AsvtIDk9gDeVxYoLpzBUNjNZTA0oUFaiYvK0f/EjnChCGJhhJYunQTv9ZPSMr4ZdAwJNFQvKWL90HNW8MT9LthSKKhaEuXHyQpD5MMkmBIoqFYSxcX6KooXt+leCkZDEk0FG3p4oOkwjw4i+3NwJBEQwksXVqgfzRfnfiBJQwJNJTAEjbNAdZAoIA1EChgDQQKWAOBAtZAoIA1EChgDQQKWAOBAtZAoIA1EChgDQQKWAOBAtZAoIA1EChgDQQKWAOBAtZAoIA1EChgDQQKWAOBAtZAoIA1EChgDQQKWAOBAtZAoIA1EChgDQQKWAOBAtZAoIA1EChgDQQKWAOBAtZAoIA1EChgDQQKWAOBAtZAoIA1EChgDQQKWAOBAtZAoIA1EChgDQQKWAOBAtZAoIA1EChgDQQKWAOBAtZAoIA1EChgDQQKWAOBAtZAoIA1EChgDQQKWAOBAtZAoIA1EChgDQQKWAOBAtZAoIA1EChgDQQaye7u6r7+MA2PbBe3/o/N7LYjFRwFAo1lXdwG/+8GAj0TCDSW7cLUnKubh56zINAzgUCjWek2fruYq89FoSrS7asfiqv/ainWKZvZn2ZFMa8FWhb6c5VqjoKDQKDRmMpxPVlWaqwq07L6sF1U1amWok3ZzPQ/c51a1p/1lf1dg9EDgUZjRkeqhd++Whq96upUSdGlKD1WUr26r1JNZbu+ul/X4ytwGAg0nkp2vo+5Vm22rjxtb1OnuGq2StWVrUrZLqDQY0Cg8ehWW0ut6lte/XtGBWpTNs+1KJ9rgRaG26ryRR/0CBBoAlY3v+pmXleTGypQl9KuQd3FwV+gAQSagPXkR11B6j7lmjbxLqXug9486D4orTQx79QLBJqA3d3v9CSoqTyLeViD6hQ9irfaLfWIf7LUVekaNWgfEGgKSj2rqXuck+VKt+O+D6pTzDwoSS1M3aoOZ3aeNxAoYA0EClgDgQLWQKCANRAoYA0EClgDgQLWQKCANRAoYA0EClgDgQLWQKCANRAoYA0EClgDgQLWQKCANRAoYA0EClgDgQLWQKCANRAoYA0EClgDgQLWQKCANf8HpyJDk7l51poAAAAASUVORK5CYII=" 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 <pre><code>## $Clusters
 ## $Clusters$Cluster_1
 ## [1] &quot;x4&quot; &quot;x2&quot; &quot;x8&quot; &quot;x6&quot;

R/Copula.R

@@ -0,0 +1,13 @@
+// central_tendencies.h
+#ifndef CENTRAL_TENDENCIES_H
+#define CENTRAL_TENDENCIES_H
+
+#include <Rcpp.h>
+using namespace Rcpp;
+
+// Declare the functions without default values
+double NNS_gravity_cpp(NumericVector x, bool discrete);
+NumericVector NNS_mode_cpp(NumericVector x, bool discrete, bool multi);
+
+#endif
+